Case 10
Brink
11/7/2022
library(ggplot2)
library(readr)
library(knitr)
library(dplyr)##
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library(downloader)
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## git_credentialslibrary(stringr)permit_data <- tempfile()
download("https://raw.githubusercontent.com/WJC-Data-Science/DTS350/master/permits.csv",
permit_data, mode = "wb")
permits <- read_csv(permit_data)## New names:
## Rows: 327422 Columns: 8
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (3): StateAbbr, countyname, variable dbl (5): ...1, state, county, year, value
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`head(permits)## # A tibble: 6 × 8
## ...1 state StateAbbr county countyname variable year value
## <dbl> <dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl>
## 1 1 1 AL 1 Autauga County All Permits 2010 191
## 2 2 1 AL 1 Autauga County All Permits 2009 110
## 3 3 1 AL 1 Autauga County All Permits 2008 173
## 4 4 1 AL 1 Autauga County All Permits 2007 260
## 5 5 1 AL 1 Autauga County All Permits 2006 347
## 6 6 1 AL 1 Autauga County All Permits 2005 313counties <- us_counties()FIPSsetup<-permits%>%
mutate(state=as.character(state), stateFIPS=str_pad(state, 2,pad="0")) %>%
mutate(county=as.character(county), countyFIPS=str_pad(county, 3,pad="0"))
FIPSsetup## # A tibble: 327,422 × 10
## ...1 state StateAbbr county countyname variable year value stateFIPS
## <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 1 1 AL 1 Autauga County All Permits 2010 191 01
## 2 2 1 AL 1 Autauga County All Permits 2009 110 01
## 3 3 1 AL 1 Autauga County All Permits 2008 173 01
## 4 4 1 AL 1 Autauga County All Permits 2007 260 01
## 5 5 1 AL 1 Autauga County All Permits 2006 347 01
## 6 6 1 AL 1 Autauga County All Permits 2005 313 01
## 7 7 1 AL 1 Autauga County All Permits 2004 367 01
## 8 8 1 AL 1 Autauga County All Permits 2003 283 01
## 9 9 1 AL 1 Autauga County All Permits 2002 276 01
## 10 10 1 AL 1 Autauga County All Permits 2001 400 01
## # … with 327,412 more rows, and 1 more variable: countyFIPS <chr>FIPS <- FIPSsetup %>%
mutate(geoid=paste0(stateFIPS,countyFIPS))county_FIPS_data <- merge(FIPS,counties, by = "geoid") %>%
group_by(state_name,year) %>%
summarise(across(value, sum))## `summarise()` has grouped output by 'state_name'. You can override using the
## `.groups` argument.USA <- ggplot(data = county_FIPS_data, aes(x = year, y = value, color = state_name)) +
geom_line() +
geom_point() +
labs(x = "Year", y = "Permit Amount") +
scale_y_continuous(labels = comma) +
theme_bw() +
theme(legend.position = 'none')
ggplotly(USA)Missouri <- county_FIPS_data %>%
filter(state_name == 'Missouri') %>%
ggplot(aes(x = year, y = value)) +
geom_point() +
geom_line() +
labs(title="Missouri",x = 'Year', y = 'Permit Amount') +
scale_y_continuous(labels = comma) +
theme_bw() +
theme(legend.position = 'none')
Missouri
Kansas <- county_FIPS_data %>%
filter(state_name == 'Kansas') %>%
ggplot(aes(x = year, y = value)) +
geom_point() +
geom_line() +
labs(title="Kansas",x = 'Year', y = 'Permit Amount') +
scale_y_continuous(labels = comma) +
theme_bw() +
theme(legend.position = 'none')
Kansas
Nebraska <- county_FIPS_data %>%
filter(state_name == 'Nebraska') %>%
ggplot(aes(x = year, y = value)) +
geom_point() +
geom_line() +
labs(title="Nebraska",x = 'Year', y = 'Permit Amount') +
scale_y_continuous(labels = comma) +
theme_bw() +
theme(legend.position = 'none')
Nebraska
Iowa <- county_FIPS_data %>%
filter(state_name == 'Iowa') %>%
ggplot(aes(x = year, y = value)) +
geom_point() +
geom_line() +
labs(title="Iowa",x = 'Year', y = 'Permit Amount') +
scale_y_continuous(labels = comma)
theme_bw() +
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grid.arrange(USA, Missouri, Kansas, Nebraska, Iowa, nrow = 3)
After finally being able to integrate the FIPS numbers with the permit data and after testing the results with surrounding border states of Missouri, that the midwest border states actually buck the trend shown in the United States wide chart. In the decade between 1990 and 2000, the majority of the U.S experienced a decrease in permits, but all of the states shown in the data above show growth or neutraility in that time period, with no sembelence of a decrease at all.